Hypoglycemia early alarm systems based on multivariable models

dc.contributor.author Turksoy, Kamuran
dc.contributor.author Bayrak, Elif
dc.contributor.author Rollins, Derrick K
dc.contributor.author Quinn, Lauretta
dc.contributor.author Littlejohn, Elizabeth
dc.contributor.author Rollins, Derrick
dc.contributor.author Cinar, Ali
dc.contributor.department Chemical and Biological Engineering
dc.date 2018-02-15T19:49:51.000
dc.date.accessioned 2020-06-30T01:08:29Z
dc.date.available 2020-06-30T01:08:29Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2013
dc.date.embargo 2015-02-04
dc.date.issued 2013-01-01
dc.description.abstract <p>Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.</p>
dc.description.comments <p>Reprinted (adapted) with permission from <em>Industrial and Engineering Chemistry Research</em> 52 (2013): 12329, doi: <a href="http://dx.doi.org/10.1021/ie3034015" target="_blank">10.1021/ie3034015</a>. Copyright 2013 American Chemical Society.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/cbe_pubs/210/
dc.identifier.articleid 1208
dc.identifier.contextkey 6608689
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cbe_pubs/210
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13303
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/cbe_pubs/210/0-2013_RollinsDK_HypoglycemiaEarlyAlarm.html|||Fri Jan 14 22:33:46 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/cbe_pubs/210/2013_RollinsDK_HypoglycemiaEarlyAlarm.pdf|||Fri Jan 14 22:33:48 UTC 2022
dc.source.uri 10.1021/ie3034015
dc.subject.disciplines Biological Engineering
dc.subject.disciplines Biomedical Engineering and Bioengineering
dc.subject.disciplines Chemical Engineering
dc.subject.disciplines Pediatric Nursing
dc.subject.keywords Statistics
dc.subject.keywords artificial pancreas
dc.subject.keywords blood glucose concentration
dc.subject.keywords dynmaic adaptations
dc.subject.keywords linear time series model
dc.subject.keywords model algorithms
dc.subject.keywords multi-variable models
dc.subject.keywords Savitzky-Golay filter
dc.subject.keywords artificial organs
dc.subject.keywords hospital data processing
dc.subject.keywords insulin
dc.subject.keywords alarm systems
dc.title Hypoglycemia early alarm systems based on multivariable models
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 95eeb5bf-f38c-45e2-9857-0f9223053e09
relation.isOrgUnitOfPublication 86545861-382c-4c15-8c52-eb8e9afe6b75
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